Yahoo! Inc.

Peiji Chen

Qi He

Erik Nathan Vee

Long-ji Lin

Dongni Chen

Datong Chen

Deepak Agarwal

Jayavel Shanmugasundaram

Jian Yang

Satyen Chandrakant Kale

Mark D Sordo

Jagannatha Narayanareddy

Long Ji Lin

Donald Swanson

Danny Zhang

Srinath Mandalapu

Chandrashekhar Nagarajan

Konstantin Salomatin

Amita Gajewar

Victor K Chu

Methods and systems are disclosed which allow shifting inventory to fulfill guaranteed delivery advertisement contracts. Inventory may be allocated from a supply of unallocated inventory to one or more advertisers in accordance with guaranteed delivery agreements. Inventory may be reserved for the one or more advertisers from the remaining supply of unallocated inventory. Inventory may then be allocated to an additional advertiser by using unallocated inventory or shifted inventory, or a combination of unallocated inventory and shifted inventory. The shifted inventory is shifted out of the allocation for the first advertiser and the shifted inventory is replaced by the reserve inventory for the respective advertiser.

Methods and systems for ad placement planning

Systems and methods for automatically forecasting the future availability of one or more resources, such as Internet advertising opportunities, are described herein. In accordance with various embodiments, a forecasting model that accounts for event-driven resource availability is trained based both on historical supply data and calendar information specifying events and event duration. The trained forecasting model is then used to forecast the availability of resources at one or more specified future time periods. In accordance with certain embodiments, the forecasting model comprises a Gaussian process model that has an event-driven kernel as a covariance function.

Embedding calendar knowledge in event-driven inventory forecasting

A simulation framework for evaluating revenue that may use a pricing engine that runs at least one pricing algorithm with particular configurations and under particular model market conditions to provide revenue projections.

Pricing engine revenue evaluation

Techniques are described herein for using an impression-trend technique to provide a display advertising supply forecast. A display advertising supply forecast is an estimate of a number of impressions, which are to occur in a future time period, that have specified attribute values. For example, the specified attribute values may be descriptive of impressions with respect to which an advertiser wishes to place ads. An impression-trend technique is a forecasting technique that uses trends regarding past impressions to forecast a number of future impressions that have specified attribute values. The past impressions include attribute values that are related to the specified attribute values.

Impression-trend techniques for providing a display advertising supply forecast

An improved system and method for forecasting an inventory of online advertisement impressions for targeting profiles of attributes is provided. An index of advertisement impressions on display advertising properties may be built for a targeting profile of attributes from forecasted impression pools. Impression pools of advertisements sharing the same attributes and trend forecast data for web pages and advertisement placements on the web pages may be integrated to generate the forecasted impression pools. An index of several index tables may be generated from forecasted impression pools. A query may be submitted to obtain an inventory forecast of advertisement impressions for targeting profiles of attributes and the index may be searched to match forecasted impression pools for the targeted profile of attributes. Then the inventory forecast of advertisement impressions on display advertising properties may be returned as query results for the targeting profile of attributes.

System and method for forecasting an inventory of online advertisement impressions for targeting impression attributes

Data quality measurement is provided for use in a data processing stream, which comprises at least one upstream data processing system and at least one downstream data processing system. An input alert component can be used to provide a measurement of data prior to its input to a data processing system (e.g., a downstream data processing system or an upstream data processing system). An output alert component can be used to provide a measurement on data output by a data processing system. A self-consistency component can be used to measure consistency between items of input, or output data. An end-to-end component can be used to measure data quality using data items from both input data and output data. These components can be used in some combination, or independent of the other, and in any order. In addition, the data quality measurements can be performed separate from that processing performed by either the upstream or downstream processing system.

Method and system of measuring data quality

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Saratoga, CA, United States of America

Peiji Chen